{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 1: What is a Network?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure plotting in Jupyter\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams.update({\n",
    "    'figure.figsize': (7.5, 7.5),\n",
    "    'axes.spines.right': False,\n",
    "    'axes.spines.left': False,\n",
    "    'axes.spines.top': False,\n",
    "    'axes.spines.bottom': False})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating your first network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QYNOmTTAx4d8uRPqirq4Ozs7OOHz4MNzd3UXHMWS8iNvYvPfee8jJycG6detY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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc17a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G = nx.Graph()\n",
    "\n",
    "G.add_node('A')\n",
    "G.add_nodes_from(['B', 'C'])\n",
    "\n",
    "G.add_edge('A', 'B')\n",
    "G.add_edges_from([('B', 'C'), ('A', 'C')])\n",
    "\n",
    "plt.figure(figsize=(7.5, 7.5))\n",
    "nx.draw_networkx(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seed random number generator\n",
    "import random\n",
    "from numpy import random as nprand\n",
    "seed = hash(\"Network Science in Python\") % 2**32\n",
    "nprand.seed(seed)\n",
    "random.seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add several nodes and edges at the same time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L78yCgMRYEoHryJEj+uGHH9S3b1/TUYzo2LGjhgwZomXLlpmOAhhBucGSMjIyNGLECAUF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11264b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G.add_edges_from([('B', 'D'), ('C', 'E')])\n",
    "nx.draw_networkx(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
